MTS Decomposition and Recombining Significantly Improves Training Efficiency in Deep Learning: A Case Study in Air Quality Prediction over Sub-Tropical Area

It is crucial to speed up the training process of multivariate deep learning models for forecasting time series data in a real-time adaptive computing service with automated feature engineering. Multivariate time series decomposition and recombining (MTS-DR) is proposed for this purpose with better...

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Κύριοι συγγραφείς:	Benedito Chi Man Tam, Su-Kit Tang, Alberto Cardoso
Μορφή:	Artigo
Γλώσσα:	Inglês
Έκδοση:	MDPI AG 2024-04-01
Σειρά:	Atmosphere
Θέματα:	MTS decomposition deep learning model training air quality prediction
Διαθέσιμο Online:	https://www.mdpi.com/2073-4433/15/5/521
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